Incorporating imaging devices within portable electronic devices, such as digital cameras, cellular telephones and portable digital assistants, is especially challenging because these devices typically have only limited amounts of memory resources, processing resources, and power resources that can be diverted to image processing. To meet memory constraints, imaging devices for portable electronic devices typically include image processors that compress the images (e.g., in the JPEG compression format) before they are stored. In most image compression methods, certain image data is discarded selectively to reduce the amount of data that is needed to represent the image while avoiding substantial degradation of the appearance of the image. In general, the compression level of an image compression process varies with image content. For example, images with less detail can be compressed to a greater extent than images with more detail. Similarly, some areas of an image may be compressed to a greater extent that other areas.
Transform coding, as exemplified by the JPEG image compression method, involves representing an image by a set of transform coefficients. The transform coefficients are quantized individually to reduce the amount of data that is needed to represent the image. A representation of the original image is generated by applying an inverse transform to the transform coefficients. Block transform coding is a common type of transform coding method. In a typical block transform coding process, an image is divided into small rectangular regions (or “blocks”), which are subjected to forward transform, quantization, and coding operations. Many different kinds of block transforms may be used to encode the blocks. Among the common types of block transforms are the cosine transform (which is the most common), the Fourier transform, the Hadamard transform, and the Haar wavelet transform. These transforms produce an M×N array of transform coefficients from an M×N block of image data, where M and N have integer values of at least 1.
In addition to storing captured images in a compressed format, some digital cameras store compressed thumbnail images corresponding to reduced-resolution versions of the captured images. Many of these digital cameras impose a bit budget constraint on these compressed thumbnail images. In order to meet these bit budget constraints, some digital camera systems set to zero selected ones of the non-zero discrete cosine transform (DCT) coefficients of the thumbnail images regardless of their value. The process of selecting these DCT coefficients starts with the highest frequency coefficients and continues down to lower-frequency coefficients until the compressed image size is below maximum bit budget. In this approach, the number of passes through the DCT coefficient data depends on the bit budget, the original compressed image size, and the image content.
In another approach, the size of an existing JPEG file (or a set of DCT coefficients) is reduced to satisfy a bit budget by setting to zero all of the DCT coefficients that have values below a threshold and that occur after a cutoff ordinal number. In this approach, the cutoff ordinal number is determined by tracking the number of bits that are saved by each incremental reduction of the cutoff ordinal number and comparing the existing file size with the required bit budget. This approach requires two passes through the DCT coefficient data: a first pass during which the bit number savings are determined; and a second pass during which certain coefficients are set to zero.
In some application environments (e.g., camera-equipped cellular telephones), cost constraints prohibit the inclusion of sufficient memory resources, processing resources, and power resources to perform multiple passes through the image data. The above-described methods for reducing the size of a compress image therefore are not optimally suited for these types of application environments.